The technological capability availed by today's computing systems and data communications systems (e.g., networks, the Internet) has greatly impacted the ability to communicate to a vast number of users. Technology permits implementing systems that communicate information, such as in the form of messages and notifications, to a vast numbers of users in order to elicit a desired response from the users. For example, a communications and management system may be built to intervene in the daily routine of a diabetes patient in order to effectively manage the patient's blood glucose levels. To this end, management systems may send various types of notifications, at various times, to remind and inform a user so as to effectively manage the user for a desired outcome. In addition to transmitting notifications and messages to users, these communication and management systems may also include a means to receive user responses in order to receive feedback as to whether the user performed the desired task. For example, in a diabetes management system, the system may record a response, using technology, that indicates the user took their medication at a specific time of day. The process of transmitting notifications and messages to users to effectuate a user response, as well as receiving and recording those responses constitute the basis for such a management system.
It is desirable to use such computing and networking capabilities to gain a deeper understanding of the impact of event notifications on events. For example, if a system propagates messages to a user to remind the user to take their medication at specific times of the day, it is desirable to understand what messages are effective at getting the patient to properly take their medication. Since an event campaign may comprise many different types of messages and notifications, it is not clear which messages or notifications were effective in getting the desired response. As such, a system administrator may desire to know what messages, sent when, and in what combinations, were most effective at eliciting the desired result. Given the capabilities of computing and networking, there are many mediums, or channels, that may be used to deliver messages to users. For example, messages may be delivered into an environment, such as a house, using various modes of communications, such as Internet messages (e.g., display messages), email, text messages on mobile devices, notifications on television, telephone calls, announcement over audio/visual medium, etc. These systems may also deploy messages to users in order to elicit commercial activity from a user, such as conversions to purchase a product or service.
Various techniques for calculating the attribution of event campaign stimuli (e.g., event notifications) to responses have been considered. Such attribution calculations are largely enabled not only by the voluminous online user activity data available (e.g., cookies, pixel tags, mobile tracking, etc.), but also by various offline data available (e.g., in-store purchase records, compliance records, etc.). However, in “noisy” response channels (e.g., organic search, surveys, compliance feedback, etc.) that may include an aggregate response from multiple event campaigns and event stimuli, legacy approaches have limitations. Such legacy approaches might provide attribution at a channel level (e.g., TV, radio, print, search, etc.), yet not provide attribution at a more granular sub-channel level. For example, a legacy approach might attribute call center purchases to a TV infomercial providing the toll-free number of the call center at the “TV” media channel level, yet not identify more specific airings and related characteristics (e.g., TV station, spot, campaign, creative, etc.) that contributed most to the purchases. Techniques are needed to address the problem of determining sub-channel stimulus attribution in aggregated response channels.